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Gene Regulatory Networks pp 25–48Cite as

Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discuss causal perspectives on network inference, including issues around model semantics that can arise due to missing variables. We present a case study of applying time-varying DBNs to gene expression measurements over the life cycle of Drosophila melanogaster. We finish with a discussion of future perspectives, including possible applications of time-varying network inference to single-cell gene expression data.

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Author information

Authors and Affiliations

  1. Lancaster Medical School, Lancaster University, Lancaster, UK

    Frank Dondelinger

  2. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Sach Mukherjee

Authors
  1. Frank Dondelinger
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  2. Sach Mukherjee
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Corresponding author

Correspondence to Frank Dondelinger .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

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Dondelinger, F., Mukherjee, S. (2019). Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_2

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  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_2

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

  • Online ISBN: 978-1-4939-8882-2

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